AI Producer Matching Using Consumer Clusters and Ranked Scoring
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Solution Overview
Problem
Existing systems struggle to accurately match consumers with suitable producers for services due to limited access to producer information and inconsistent determination of initial producers for proposal requests, leading to sub-optimal and inconsistent matching processes.
Innovation Solution
Utilizing AI models to analyze consumer and producer data, along with external factors, to generate a ranked list of producers that are likely to provide suitable services by implementing a ranking model and clustering consumers into clusters based on shared characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI models and clustering are used to improve matching accuracy, then matching precision is improved, but system complexity increases
Solution Approach 1:
The system segments consumers into distinct clusters based on shared characteristics (e.g., industry, size, location) before matching with producers. This segmentation enables more precise matching by treating different consumer groups differently, rather than using a uniform matching approach for all consumers.
Solution Approach 2:
The patent introduces an intermediary matching system that uses AI models and clustering algorithms as mediators between consumers and producers. This intermediary layer processes consumer data, identifies clusters, and selects appropriate producers, thereby improving matching precision while managing the complexity through automated intermediate processing.
2Reliability
If multiple AI models are used to generate comprehensive match scores, then matching reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing consumer data and pre-identifying cluster memberships before the actual matching process. Consumer characteristics are analyzed and clusters are formed in advance, so that during matching, the system only needs to retrieve pre-computed cluster information rather than performing comprehensive analysis in real-time.
Solution Approach 2:
The patent applies partial action by using multiple AI models selectively - different models are applied to different aspects of the matching problem (e.g., one model for consumer clustering, another for producer matching, another for score generation). This allows the system to achieve comprehensive matching reliability by combining partial results from specialized models rather than using one overly complex comprehensive model.
Data Source
AI summary
A subset of producers that are a potential match to provide services to a first consumer are identified among producers via software-as-a-service (SaaS) management platform. A first output indicating a consumer cluster identifier corresponding to the first consumer is obtained from a first trained artificial intelligence (AI) model. The consumer cluster identifier identifies, among multiple consumer clusters, a first consumer cluster that corresponds to the first consumer. An estimate that a response from a first producer of the subset of producers will satisfy consumer preferences associated with the first consumer cluster is generated. A score for each of the subset of producers is generated based the estimate. The score indicates a likelihood that a respective producer of the subset of producers is a match for the first consumer. A notification indicating the scores for the subset of producers is provided.